76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
"""
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ONNX inference for MAGICMATCH color-match graph (Comfy IMAGE tensors).
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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import numpy as np
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try:
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import onnxruntime as ort
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except ImportError as e:
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raise ImportError("MAGICMATCH: pip install onnxruntime") from e
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PACKAGE_ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_ONNX = PACKAGE_ROOT / "models" / "color_match.onnx"
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IMAGE_SIZE = 256
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INPUT_NAMES = ("input_img:0", "ref_img:0")
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OUTPUT_LUT3D = "3D_LUT"
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OUTPUT_LUT1D = "1D_LUT"
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_SESSION: ort.InferenceSession | None = None
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def _resize_hwc_to_nhwc(image_hwc: np.ndarray, size: int = IMAGE_SIZE) -> np.ndarray:
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"""H×W×3 float [0,1] → [1, size, size, 3] PIL bilinear (same as probe load_rgb_nhwc)."""
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from PIL import Image
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image_hwc = np.asarray(image_hwc, dtype=np.float32)
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arr = (np.clip(image_hwc, 0, 1) * 255.0).astype(np.uint8)
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pil = Image.fromarray(arr, "RGB")
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pil = pil.resize((size, size), Image.Resampling.BILINEAR)
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out = np.asarray(pil, dtype=np.float32) / 255.0
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return np.clip(out[np.newaxis, ...], 0.0, 1.0)
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def get_session(onnx_path: str | Path | None = None) -> ort.InferenceSession:
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global _SESSION
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path = Path(onnx_path or DEFAULT_ONNX)
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if not path.is_file():
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raise FileNotFoundError(
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f"MAGICMATCH ONNX not found: {path}\n"
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"Expected: MAGICMATCH/models/color_match.onnx in this custom node folder."
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)
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if _SESSION is None:
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_SESSION = ort.InferenceSession(str(path), providers=["CPUExecutionProvider"])
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return _SESSION
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def run_inference_from_images(
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source_hwc: np.ndarray,
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reference_hwc: np.ndarray,
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*,
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session: ort.InferenceSession | None = None,
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) -> tuple[np.ndarray, np.ndarray]:
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"""Run graph on H×W×3 RGB (float 0–1). Returns lut3d, lut1d."""
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sess = session or get_session()
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feeds = {
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INPUT_NAMES[0]: _resize_hwc_to_nhwc(source_hwc),
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INPUT_NAMES[1]: _resize_hwc_to_nhwc(reference_hwc),
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}
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out_map = {o.name: o for o in sess.get_outputs()}
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names = [out_map[OUTPUT_LUT3D].name, out_map[OUTPUT_LUT1D].name]
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lut3d, lut1d = sess.run(names, feeds)
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return np.asarray(lut3d, dtype=np.float32), np.asarray(lut1d, dtype=np.float32)
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def build_merged_lut(source_hwc: np.ndarray, reference_hwc: np.ndarray) -> np.ndarray:
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from .lut import get_merged_lut, nn_outputs_to_flat
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lut3d, lut1d = run_inference_from_images(source_hwc, reference_hwc)
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return get_merged_lut(*nn_outputs_to_flat(lut1d, lut3d))
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